Impact of the Grade of Degenerative Lumbar Disease on the Occurrence of Spinal Deformity

Battisti, Raphael; Oliveira, Carlos Eduardo Algaves Soares de; Pratali, Raphael de Rezende

doi:10.1590/s1808-185120212001233115

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…The lumbar spine's susceptibility to diverse disorders, ranging from degenerative disc diseases to spinal stenosis, underscores the urgency for accurate identification and classification. Lumbar degenerative diseases encompass a spectrum of conditions, such as lumbar disc herniation, lumbar stenosis, and spondylosis, each presenting unique challenges [2], [3]. Surgical interventions, such as oblique lumbar interbody fusion (OLIF) and posterior lumbar interbody fusion (PLIF), offer viable options for lumbar fusion, with OLIF demonstrating advantages in radiographic outcomes and complication rates [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Lumbar Disease Classification Using an Involutional Neural Based VGG Nets (INVGG)

Abuhayi,

Bezabh,

Ayalew

2024

IEEE Access

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Degenerative diseases of the lumbar spine, such as spondylolisthesis, disc degeneration, and lumbar spinal stenosis, are major contributors to global disability. Accurate classification of lumbar diseases is crucial for effective medical diagnosis. This paper introduces an innovative methodology for lumbar disease classification, addressing the limitations of traditional convolutional neural networks (CNNs). We propose a novel approach, InVGG, which combines involutional neural networks with the VGG architecture. Unlike traditional CNNs, InVGG utilizes involution kernels that are location-specific and channel-agnostic, enhancing its adaptability to varied visual patterns in medical images. Our study focuses on a four-class lumbar disease classification problem using sagittal T2 MRI images. The evaluation of InVGG is compared with traditional CNNs (VGG model) and machine learning algorithms, demonstrating superior performance in terms of accuracy, precision, recall, and AUC ROC values. InVGG achieves an impressive 96% accuracy on the testing set and 99% on the training set, showcasing its potential for accurate spinal lumbar disease classification. The reduced parameter count of InVGG compared to CNNs (VGG) makes it more resourceefficient, especially in scenarios with limited computational resources and datasets. The promising results position InVGG as a valuable tool for precise lumbar disease classification, with implications for improving patient care in resource-constrained scenarios.

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